Field of the Invention
The present invention concerns an examination of an object with a magnetic resonance (MR) system by MR fingerprinting.
Description of the Prior Art
In clinical imaging, MR images generally have only a qualitative contrast. The exact pixel values are subject to many influences, such as the parameter settings selected for the measurement (e.g. TE, TR, bandwidth) and factors that the user is unable to influence (e.g. coil sensitivity, software versions, scanner type).
In many applications, it would be desirable to have a so-called quantitative MR image in which the pixel values would correspond to “genuine” physical variables (for example the T1 relaxation time, the T2 relaxation time, the off-resonance, the proton density). One quantitative MR imaging method of this kind is so-called MR fingerprinting, which is described in “Magnetic Resonance Fingerprinting”, Ma et al, Nature 2013 Mar. 14; 495(7440): 187-192. doi:10.1038/nature11971. With MR fingerprinting, numerous measurements are performed wherein measuring parameters or recording parameters (e.g. flip angle, TR (time to repetition), TE (echo time), TI (inversion time), an embodiment and/or a number of RF pulses, an embodiment and/or a number of gradient pulses, diffusion encoding) are varied in a pseudo-random manner. For each measurement, the MR signal is determined for each voxel so that for each voxel or pixel an MR signal curve characterizing the voxel or pixel is obtained, which can be considered to a “fingerprint”. A fingerprint of this kind can be assigned, with the use of a database, to a specific n-tuple of physical values (e.g. T1 relaxation time, T2 relaxation time, off-resonance, proton density), and hence to a specific substance (e.g. CSF, cerebral grey matter, fat).
The database stores MR signal curves for the pseudo-random variation of the recording parameters for many of these n-tuples of physical values. These stored MR signal curves are usually generated by simulation with the use of Bloch equations on the basis of the pseudo-random variation of the recording parameters. In order, for example, to have an MR signal curve for each combination of a T1 relaxation time in a range of from 100 ms to 5000 ms and a T2 relaxation time in a range of from 10 ms to 2000 ms with a resolution of 10 ms, it is necessary for as many as almost 100,000 MR signal curves to be available.
Pattern recognition algorithms are now used to determine the stored or simulated MR signal curve conforming most closely to the respective MR signal curve measured for a specific voxel or pixel.
According to the prior art, the number of MR images to be recorded, and hence the length of the MR signal curve measured for each voxel, is either specified by the user or a heuristic standard value is used. In this case, it is generally assumed that the greater the number of MR images recorded, and hence the longer the MR signal curve measured for the voxel, the greater the accuracy and the image quality.